Eco-conscious consumers’ green real estate decisions in India: the role of social commerce
Bibliographic record
Abstract
Purpose The primary purpose of this paper is to examine the role of perceived trust, information quality, positive word of mouth and societal norms toward real estate purchase intention. The study also examines how pro-environmental self-identity mediates the relationship between positive word of mouth and real estate purchase intent, as well as between societal norms and real estate purchase intention. This research aims to delve into these intricate dynamics through a multidimensional lens. Design/methodology/approach The research employs existing scholarly works and measurable variables evaluated through a five-point Likert scale, hypothesis testing and mediation analysis to examine the proposed framework. A structured survey comprising six sections was administered, yielding 385 valid responses. The data analysis process included the use of confirmatory factor analysis and structural equation modelling techniques. Findings The analysis indicates that pro-environmental self-identity has the most significant influence on real estate purchase intention, closely followed by positive word of mouth. Incorporating eco-friendly themes in marketing campaigns significantly boosts purchase intentions. However, perceived trust does not significantly impact purchase intentions. Other factors, such as information quality and societal norms, also play significant roles, underscoring the importance of understanding the complex dynamics shaping consumer decisions in the real estate market. Research limitations/implications This research exclusively targets responses from young consumers in specific regions of India. Future studies should aim for a more extensive geographic scope, encompassing a diverse global population for a broader understanding of the subject. Originality/value Based on previous literature, this study is the first to identify the elements influencing the inclination to buy environmentally friendly real estate through social commerce.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".